# Number of threads for hot thread monitoring.
crawler.hotthread.threads=3
# Timeout for hot thread monitoring (e.g., 30s).
crawler.hotthread.timeout=30s
# Type of hot thread monitoring (e.g., cpu).
crawler.hotthread.type=cpu
# Metadata fields to exclude from document content.
crawler.metadata.content.excludes=resourceName,X-Parsed-By,Content-Encoding.*,Content-Type.*,X-TIKA.*,X-FESS.*
# Mapping for document metadata names.

	erasureLockOwner string

	// List of endpoints provided on the command line.
	endpoints PoolEndpoints

	// String version of all the endpoints, an optimization
	// to avoid url.String() conversion taking CPU on
	// large disk setups.
	endpointStrings []string

	// Total number of sets and the number of disks per set.
	setCount, setDriveCount int
	defaultParityCount      int

	poolIndex int

    resource availability for critical system pods.
* **[alpha]** `--experimental-nvidia-gpus` flag is replaced by `Accelerators` alpha
  feature gate along with addition of support for multiple Nvidia GPUs.
  - To use GPUs, pass `Accelerators=true` as part of `--feature-gates` flag.
  - More information [here](https://vishh.github.io/docs/user-guide/gpus/).
* A pod’s Quality of Service Class is now available in its Status.

* Fixed a bug in the single-numa-node policy of the TopologyManager.  Previously, pods that only requested CPU resources and did not request any third-party devices would fail to launch with a TopologyAffinity error. Now they will launch successfully. ([#83697](https://github.com/kubernetes/kubernetes/pull/83697), [@klueska](https://github.com/klueska))

- Fixed a bug in kubelet that will saturate CPU utilization after containerd got restarted. ([#97174](https://github.com/kubernetes/kubernetes/pull/97174), [@hanlins](https://github.com/hanlins)) [SIG Node]

Uvicorn process running your FastAPI application). They would all be **identical containers**, running the same thing, but each with its own process, memory, etc. That way you would take advantage of **parallelization** in **different cores** of the CPU, or even in **different machines**.

And the distributed container system with the **load balancer** would **distribute the requests** to each one of the containers with your app **in turns**. So, each request could be handled by one of the...

    - [API changes and improvements for ephemeral containers](#api-changes-and-improvements-for-ephemeral-containers)
  - [Known Issues](#known-issues)
    - [CPU and Memory manager are not working correctly for Guaranteed Pods with multiple containers](#cpu-and-memory-manager-are-not-working-correctly-for-guaranteed-pods-with-multiple-containers)

	}
}

func getMinIOProcessCPUTime() MetricDescription {
	return MetricDescription{
		Namespace: nodeMetricNamespace,
		Subsystem: processSubsystem,
		Name:      cpu,
		Help:      "Total user and system CPU time spent in seconds",
		Type:      counterMetric,
	}
}

func getMinioProcMetrics() *MetricsGroupV2 {
	mg := &MetricsGroupV2{
		cacheInterval: 10 * time.Second,
	}

		// Run the data scanner in a loop
		for {
			runDataScanner(ctx, objAPI)
			duration := max(time.Duration(r.Float64()*float64(scannerCycle.Load())),
				// Make sure to sleep at least a second to avoid high CPU ticks.
				time.Second)
			time.Sleep(duration)
		}
	}()
}

func getCycleScanMode(currentCycle, bitrotStartCycle uint64, bitrotStartTime time.Time) madmin.HealScanMode {

un proceso Uvicorn ejecutando tu aplicación FastAPI). Todos serían **contenedores idénticos**, ejecutando lo mismo, pero cada uno con su propio proceso, memoria, etc. De esa forma, aprovecharías la **paralelización** en **diferentes núcleos** de la CPU, o incluso en **diferentes máquinas**.

Y el sistema de contenedores distribuido con el **load balancer** **distribuiría las requests** a cada uno de los contenedores **replicados** que ejecutan tu aplicación **en turnos**. Así, cada request...